// to the given dividend, so that we don't have half of our divisions being
  // trivial because the divisor is bigger than the dividend.
  // Using remainder here does not give us a uniform distribution but it should
  // not have a big impact on the measurement.
  private static long randomDivisor(long dividend) {
    long r = randomSource.nextLong();
    if (dividend == -1) {
      return r;

  // to the given dividend, so that we don't have half of our divisions being
  // trivial because the divisor is bigger than the dividend.
  // Using remainder here does not give us a uniform distribution but it should
  // not have a big impact on the measurement.
  private static long randomDivisor(long dividend) {
    long r = randomSource.nextLong();
    if (dividend == -1) {
      return r;

{* ../../docs_src/header_param_models/tutorial001_an_py310.py hl[9:14,18] *}

**FastAPI** will **extract** the data for **each field** from the **headers** in the request and give you the Pydantic model you defined.

## Check the Docs { #check-the-docs }

You can see the required headers in the docs UI at `/docs`:

<div class="screenshot">

  // For example: (long) data[i++] | (long) data[i++] << 8 | ...
  // If data[i] == (byte) 0x80, the first cast will sign-extend it to 0xffffffffffffff80,
  // masking the remaining seven bytes.
  // To test this, we give an input where bit 7 is not cleared. For example:
  // (1) 00 01 02 03 04 05 06 07 80
  // (2) 00 01 02 03 04 05 06 07 81
  // (3) 00 01 02 03 04 05 06 07 ff (or anything in between)

            @Throws(IOException::class)
            override fun authenticate(route: Route?, response: Response): Request? {
              if (response.request.header("Authorization") != null) {
                return null // Give up, we've already attempted to authenticate.
              }

              println("Authenticating for response: $response")
              println("Challenges: ${response.challenges()}")

{* ../../docs_src/security/tutorial001_an_py39.py hl[12] *}

But that is still not that useful.

Let's make it give us the current user.

## Create a user model { #create-a-user-model }

First, let's create a Pydantic user model.

The same way we use Pydantic to declare bodies, we can use it anywhere else:

    assertEquals(
        thread.getName().substring(0, thread.getName().lastIndexOf('-')),
        thread2.getName().substring(0, thread.getName().lastIndexOf('-')));

    // Building again should give us a different pool ID.
    ThreadFactory threadFactory2 = builder.build();
    Thread thread3 = threadFactory2.newThread(monitoredRunnable);
    checkThreadPoolName(thread3, 1);

- Click `Create subproject` button at the bottom of the "Subprojects" region of [this page](https://builds.gradle.org/admin/editProject.html?projectId=Gradle&tab=projectGeneralTab)
  - Select `Manually`.
  - Give it a name. The name will be displayed on TeamCity web UI. We highly recommend it be capitalized from the branch name, i.e. `MyTestBranch`.

{* ../../docs_src/cookie_param_models/tutorial001_an_py310.py hl[9:12,16] *}

**FastAPI** will **extract** the data for **each field** from the **cookies** received in the request and give you the Pydantic model you defined.

## Check the Docs { #check-the-docs }

You can see the defined cookies in the docs UI at `/docs`:

<div class="screenshot">
<img src="/img/tutorial/cookie-param-models/image01.png">

 |
 |                 NOTE: This location can be overridden with the CLI option:
 |
 |                 -it /path/to/installation/toolchains.xml
 |
 | The sections in this sample file are intended to give you a running start at
 | getting the most out of your Maven installation.
 |-->
<toolchains xmlns="http://maven.apache.org/TOOLCHAINS/1.1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"